How UBS uses AI

UBS's US wealth management business runs STAAT Insights, a machine learning engine from its Smart Technologies and Advanced Analytics Team (STAAT) that surfaces client opportunities and alerts to financial advisors, such as shifting liquidity needs from a maturing CD, a concentrated stock position or a life event, and sends pre meeting client briefings with suggested talking points. In a December 2025 interview its chief data and analytics officer said 80% of US advisors actively use the engine.

Two time saving figures UBS has published are about its AI tools in general, not STAAT Insights alone, so they are not recorded as metrics here: an advisor recruiting page says some advisors using UBS AI have saved three to four hours per client meeting, and the same executive conservatively estimated that US advisors save 10,000 hours a month by using AI to prepare for client meetings.

UBS built two domain specific assistants, together called UBS Red, on Azure AI Search and Azure OpenAI Service to give client advisors fast, multilingual access to the bank's investment advice and product content during client work. UBS digitized about 60,000 investment advice and product documents into a queryable knowledge base, which it says saves considerable time in meeting preparation and research. Within 10 months the wider Azure OpenAI footprint reached key wealth, banking and operations divisions in the Switzerland, Hong Kong and Singapore booking centers. No usage or time saving figure specific to UBS Red is published.

In 2018 UBS ran a three month experiment called UBS Companion at a branch in Zurich. Client advisors in Wealth Management Switzerland brought two avatars into meetings with a set number of clients, shown on an LCD screen: Fin, a digital assistant that can handle simple banking transactions such as ordering a credit card, and Daniel Kalt, a digital version of the bank's Regional Chief Investment Officer Switzerland that supports market topics and provides CIO House View content on demand. UBS built them with IBM and FaceMe to test how clients accept digital assistants and how human like they should be. The page reports no results.